000			03176nam a22004578i 4500
001			CR9781316104514
005			20250919142052.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			140520s2015||||enk o ||1 0|eng|d
020			_a9781316104514 (ebook)
020			_z9781107088061 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA601 _b.S785 2015
082	0	0	_a621.36/7 _223
100	1		_aStarck, Jean-Luc, _eauthor.
245	1	0	_aSparse image and signal processing : _bwavelets and related geometric multiscale analysis / _cJean-Luc Starck, Fionn Murtagh, Jalal Fadili.
246	3		_aSparse Image & Signal Processing
250			_aSecond edition.
264		1	_aCambridge : _bCambridge University Press, _c2015.
300			_a1 online resource (xix, 428 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520			_aThis thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB® and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications.
505	0		_aIntroduction to the world of sparsity -- The wavelet transform -- Redundant wavelet transform -- Nonlinear multiscale transforms -- Multiscale geometric transforms -- Sparsity and noise removal -- Linear inverse problems -- Morphological diversity -- Sparse blind source separation -- Dictionary learning -- Three-dimensional sparse representations -- Multiscale geometric analysis on the sphere -- Compressed sensing -- This book's take home message.
650		0	_aTransformations (Mathematics)
650		0	_aSignal processing.
650		0	_aImage processing.
650		0	_aSparse matrices.
650		0	_aWavelets (Mathematics)
700	1		_aMurtagh, Fionn, _eauthor.
700	1		_aFadili, Jalal, _eauthor.
776	0	8	_iPrint version: _z9781107088061
856	4	0	_uhttps://doi.org/10.1017/CBO9781316104514
907			_a.b16848056 _b2020-12-22 _c2020-12-22
942			_n0
998			_a1 _b2020-12-22 _cm _da _feng _genk _y0 _z.b16848056
999			_c652148 _d652148